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transformers/docs/source/en/model_doc/glm4v_moe.md

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+*This model was released on 2025-07-28 and added to Hugging Face Transformers on 2025-08-08.*
+
+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">    </div>
+</div>
+
+# Glm4vMoe
+
+## Overview
+
+Vision-language models (VLMs) have become a key cornerstone of intelligent systems. As real-world AI tasks grow increasingly complex, VLMs urgently need to enhance reasoning capabilities beyond basic multimodal perception — improving accuracy, comprehensiveness, and intelligence — to enable complex problem solving, long-context understanding, and multimodal agents.
+
+Through our open-source work, we aim to explore the technological frontier together with the community while empowering more developers to create exciting and innovative applications.
+
+[GLM-4.5V](https://huggingface.co/papers/2508.06471) ([Github repo](https://github.com/zai-org/GLM-V)) is based on ZhipuAI’s next-generation flagship text foundation model GLM-4.5-Air (106B parameters, 12B active).  It continues the technical approach of [GLM-4.1V-Thinking](https://huggingface.co/papers/2507.01006), achieving SOTA performance among models of the same scale on 42 public vision-language benchmarks.  It covers common tasks such as image, video, and document understanding, as well as GUI agent operations.
+
+![bench_45](https://raw.githubusercontent.com/zai-org/GLM-V/refs/heads/main/resources/bench_45v.jpeg)
+
+Beyond benchmark performance, GLM-4.5V focuses on real-world usability. Through efficient hybrid training, it can handle diverse types of visual content, enabling full-spectrum vision reasoning, including:
+- **Image reasoning** (scene understanding, complex multi-image analysis, spatial recognition)
+- **Video understanding** (long video segmentation and event recognition)
+- **GUI tasks** (screen reading, icon recognition, desktop operation assistance)
+- **Complex chart & long document parsing** (research report analysis, information extraction)
+- **Grounding** (precise visual element localization)
+
+The model also introduces a **Thinking Mode** switch, allowing users to balance between quick responses and deep reasoning. This switch works the same as in the `GLM-4.5` language model.
+
+## Glm4vMoeConfig
+
+[[autodoc]] Glm4vMoeConfig
+
+## Glm4vMoeTextConfig
+
+[[autodoc]] Glm4vMoeTextConfig
+
+## Glm4vMoeTextModel
+
+[[autodoc]] Glm4vMoeTextModel
+    - forward
+
+## Glm4vMoeModel
+
+[[autodoc]] Glm4vMoeModel
+    - forward
+
+## Glm4vMoeForConditionalGeneration
+
+[[autodoc]] Glm4vMoeForConditionalGeneration
+    - forward